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Graph neural networks (GNN) are a powerful tool for combining imaging and non-imaging medical information for node classification tasks. Cross-network node classification extends GNN techniques to account for domain drift, allowing for node classification on an unlabeled target network. In this paper we present OTGCN, a powerful, novel approach to cross-network node classification. This approach leans on concepts from graph convolutional networks to harness insights from graph data structures while simultaneously applying strategies rooted in optimal transport to correct for the domain drift that can occur between samples from different data collection sites. This blended approach provides a practical solution for scenarios with many distinct forms of data collected across different locations and equipment. We demonstrate the effectiveness of this approach at classifying Autism Spectrum Disorder subjects using a blend of imaging and non-imaging data. 

Influence propagation is a network phenomenon governing how information is diffused in a network. With the advent of deep learning, there has been growing interest in applying graph neural networks to extract salient feature representation of the nodes for a variety of network mining tasks, such as forecasting the virality of information cascade. Given the importance of social influence, this paper presents a novel deep learning framework called IP-GNN for simulating the information propagation process in a complex network and learning a node representation that embeds information about the diffusion process under the linear threshold model. Our framework employs a modified graph convolutional network architecture with adaptive diffusion kernel to capture long-range propagation of information along with an entropy-regularized mixture of loss functions to ensure accurate prediction and faster convergence of the learning algorithm. Experimental results on 4 real-world datasets show that the model accurately mimics the output of the linear threshold model, achieving an average accuracy that exceeds 90\% on all datasets.